659건 · 관련도순
Run Python code on cloud GPUs using Modal serverless platform. Use when you need A100/T4/A10G GPU access for training ML models. Covers Modal app setup, GPU selection, data downloading inside functions, and result handling.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
First-time setup for protein design tools. Use this skill when: (1) User is new and hasn't run any tools yet, (2) Commands fail with "file not found" or "modal: command not found", (3) Modal authentication errors occur, (4) User asks how to get started or set up the environment, (5) biomodals directory is missing or tools aren't working.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Patterns for building multimodal AI applications that combine text, images, audio, and video. Covers vision APIs, audio transcription, and unified pipelines. Use when "multimodal AI, vision API, image understanding, GPT-4V, Claude vision, audio transcription, Whisper, document extraction, image to text, " mentioned.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Extending propositional and predicate logic with modal operators -- necessity and possibility, and their relatives (obligation, knowledge, belief, time). Covers Kripke possible-worlds semantics, accessibility relations, the main modal systems (K, T, S4, S5), translation from natural language, and applications in epistemic, deontic, and temporal reasoning. Use when ordinary logic is insufficient to capture distinctions like "must" vs "might," "knows" vs "believes," "always" vs "sometimes."
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
对 DiT（扩散/多模态生成）模型执行 W8A8 动态（data-free）或 W8A8 MXFP8（data-free）量化。覆盖 HunyuanVideo、Wan2.2-T2V/I2V/TI2V、FLUX.1-dev（已迁移）、SD3、Sana、HunyuanDiT、CogViewX 等。YAML apiversion 固定 multimodal_sd_modelslim_v1，统一 MultimodalPipelineInterface 重构路径（不再保留 LegacyMultimodalPipelineInterface）。模板与字段以 msmodelslim/model/hunyuan_video/model_adapter.py（唯一已迁移到重构路径且 lab_practice YAML 已 validated 的 DiT）为参考；其它 DiT 需按各自推理仓的 `parse_args` 调整字段集合。data-free 指不引入外部激活校准数据，spec.dataset（校准集短名，短名如 wan2_2_t2v；enable_dump: false 时不参与 dump）仍必填；精度调优通过下游经验库 L2 §7 + 直接调 `quantization-expert-experience-tuning-rules/scripts/apply_rollback.py`（整层回退）扩展 exclude 列表，闭环由 `quantization-accuracy-tuning-orchestrator` 调度。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build multimodal AI applications with vision and text. TRIGGERS - Use when user needs help with ai-multimodal-app related tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads. Don't use for simple text-based chat applications or workloads without real-time streaming requirements.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Choose what kind of knowledge to transfer between teacher and student models: response, feature, or relational, and decide among offline, online, self, or cross-modal distillation schemes. Best for distillation strategy selection, capacity-gap diagnosis, and transfer planning. Activate on "knowledge distillation", "teacher-student", "soft labels", "feature distillation", "online distillation", or "cross-modal transfer". NOT for generic compression checklists or unrelated training work.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Modelo da OpenAI que conecta visão e linguagem. Permite classificação de imagens com zero-shot, correspondência imagem-texto e recuperação cross-modal. Treinado em 400M pares imagem-texto. Use para busca de imagens, moderação de conteúdo ou tarefas visão-linguagem sem fine-tuning. Melhor para compreensão geral de imagens.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build multimodal AI applications with vision and text. TRIGGERS - Use when user needs help with ai-multimodal-app related tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implements accessible modals and drawers with focus trap, ESC to close, scroll lock, portal rendering, and ARIA attributes. Includes sample implementations for common use cases like edit forms, confirmations, and detail views. Use when building "modals", "dialogs", "drawers", "sidebars", or "overlays".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
React와 Next.js의 폼과 모달을 키보드와 화면 낭독기로 사용할 수 있도록 돕습니다. 레이블, 오류 표시, 포커스 이동을 구현하고 점검합니다.
Turn a written brief into an explainer video by generating narration, images, music, and optional talking presenters. Assemble scenes with Remotion, adjust their timing to the generated audio, review still frames, and render an MP4.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Design and improve in-app screens that invite users to pay or upgrade. Plan messages, timing, feature previews, and payment steps for usage limits, locked features, and trial endings, with clear options to dismiss or continue free.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Design popups, banners, and slide-in messages for email signups or purchases. Get copy, display timing, audience rules, frequency limits, mobile guidance, and test ideas, with attention to easy dismissal and accessible interaction.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Add a diffusion model or Diffusers generation pipeline to SGLang. Examine reference code, choose a suitable pipeline structure, reuse existing components, and implement model settings and processing stages, with guidance for later multi-GPU support.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Design and improve in-app upgrade prompts, paid feature screens, and trial expiration notices. Plan when they appear, what they say, how users reach payment, and which metrics to track while keeping dismissal options clear.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Plan and improve popups and banners for email signups, purchases, and announcements. The skill helps define display timing, audiences, copy, and frequency, with mobile design guidance and comparison tests to evaluate changes.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.